Large Language Models for Information Retrieval: A Survey

By Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, Ji-rong Wen. ACM Trans. Inf. Syst., 2023

602 citations recorded by the source corpus.

As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve as components of dialogue, question-answering, and recommender systems. The trajectory of IR has evolved dynamically from its origins in term-based methods to its integration with advanced neural models. While the neural models excel at capturing complex contextual signals and semantic nuances, they still face challenges such as data scarcity, interpretability, and the generation of contextually plausible yet potentially inaccurate responses. This evolution requires a combination of traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity). Meanwhile, the emergence of large language models (LLMs) has revolutionized natural language processing due to their remarkable language understanding, generation, and reasoning abilities. Consequently, recent research has sought to leverage LLMs to improve IR systems. Given the rapid evolution of this research trajectory, it is necessary to consolidate existing methodologies and provide nuanced insights through a comprehensive overview. In this survey, we delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, readers, and search agents.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/0b220041eb83c23b7b10d32a5d08c0309d528071

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Michael Bendersky
2. Kai Hui
3. Donald Metzler
4. Xinyu Ma
5. Junru Wu
6. Xuanhui Wang
7. Zhumin Chen
8. Pengjie Ren

In Information Systems

1. Michael Bendersky
2. Xuanhui Wang
3. Kai Hui
4. Donald Metzler
5. Xinyu Ma
6. Zhumin Chen
7. Pengjie Ren
8. Tianqi Liu

Citations in the corpus

• How Can Recommender Systems Benefit from Large Language Models: A Survey (2023, unknown): “Wang et al. [ 2022 ] train a general-purpose recommendation model based on items with mixture-of-modality features, which are encoded by language or vision foundation models.”
• Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agent (2023, background): “Recently, large language models (LLMs) have found increasing applications in information retrieval (Zhu et al., 2023).”
• Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach (2023, unknown): “To bridge the gap between language models and recommender systems, a promising approach is to consider behavior modeling as language modeling [6, 12, 13, 26]: it formats the recommendation tasks in the form of natural language text and then treat them as a language processing task.”
• Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting (2023, background): “…information retrieval in general with LLMs based approaches (Zhu et al., 2023), due to the importance of the applications and the power of LLMs to understand textual queries and documents (Dai et al., 2022; Tay et al., 2022b; Wang et al., 2023; Jagerman et al., 2023; Bonifacio et al., 2022).”
• A Survey on the Memory Mechanism of Large Language Model-based Agents (2024, unknown): “In addition, cognitive psychology has been studied for a long time, so many effective human memory theories and architectures have been accumulated, which can support more advanced capabilities of the agents [147].”
• Temporal Preference Optimization for Unsupervised Retrieval (2026, unknown): “Document retrieval is the process of identifying relevant documents from document collections (Gao et al., 2024; Zhao et al., 2024a;b; Zhu et al., 2025; Li et al., 2025).”
• From Semantic Retrieval to Conversational Agent: A Web-Based RAG Architecture for Interactive System Dynamics Modeling (2026, unknown): “Modern frameworks increasingly rely on advanced conversational query clarification and iterative agentic RAG synergies [18,19], alongside the need for principled evaluation of both the generative component [20] and the interactive retrieval process itself [21].”
• Information Retrieval Using Large Language Models on a Restricted Access Environment (2026, unknown): “It has become part of daily routines for most people due to the need for rapid information and decision-making [2, 3].”
• Mycroft - Retrieval Augmented Generation for SDK Documentation (2025, unknown): “It has become part of daily routines for most people due to the need for rapid information and decision-making [1, 2].”
• Retrieval And Structuring Augmented Generation with Large Language Models (2025, unknown): “In contrast to sparse lexical methods, dense retrieval [61, 304, 314] uses continuous vector embeddings to represent queries and documents.”
• A Survey of Model Architectures in Information Retrieval (2025, unknown): “Different from prior works and surveys (Lin et al., 2022; Zhu et al., 2023), we intentionally separate our discussion of model architectures from training methodologies and deployment best practices to provide a focused architectural analysis.”
• Data Kernel Perspective Space Performance Guarantees for Synthetic Data from Transformer Models (2026, unknown): “…models have enabled unprecedented improvement in performance of language technologies for a wide range of tasks (e.g. automatic speech recognition ( [1], [2], [3], [4], [5]), information retrieval ( [6], [7], [8], [9], [10]), particularly since the advent of transformer-based approaches [11], [12].”